CN111540049A - Geological information identification and extraction system and method - Google Patents
Geological information identification and extraction system and method Download PDFInfo
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Abstract
The invention provides a geological information recognition and extraction system and a geological information recognition and extraction method.
Description
Technical Field
The invention relates to the technical field of geological detection, in particular to a geological information identification and extraction system and a geological information identification and extraction method.
Background
Most of the existing geological acquisition modes adopt a geological detection instrument for direct measurement, but the detection efficiency of the direct measurement detection mode is very low, and the detection is not comprehensive enough.
Disclosure of Invention
The invention aims to provide a system and a method for identifying and extracting geological information so as to improve the efficiency and comprehensiveness of geological exploration.
In order to achieve the purpose, the invention provides the following scheme:
a system for identifying and extracting geological information, the system comprising: the system comprises an unmanned aerial vehicle, an unmanned aerial vehicle control terminal, an oblique photography component, a three-dimensional model generation module and a geological information identification and extraction module; the unmanned aerial vehicle control terminal is wirelessly connected with the unmanned aerial vehicle;
the unmanned aerial vehicle control terminal is used for controlling the flight of the unmanned aerial vehicle;
the oblique photographing component is arranged on the unmanned aerial vehicle, is in wireless connection with the three-dimensional model generation module, and is used for acquiring oblique images of a research area and sending the oblique images to the three-dimensional model generation module;
the three-dimensional model generation module is connected with the geological information recognition and extraction module, and is used for generating a three-dimensional model of a research area according to the oblique image and sending the three-dimensional model to the geological information recognition and extraction module;
and the geological information identification and extraction module is used for identifying and extracting geological information based on the three-dimensional model to obtain geological information of a research area.
Optionally, the unmanned aerial vehicle control terminal includes a mobile phone terminal and an unmanned aerial vehicle remote controller;
the mobile phone mobile terminal is provided with DJIGO software and is in wireless connection with the unmanned aerial vehicle remote controller, and the mobile phone mobile terminal is used for generating a flight shooting route according to flight parameters input by a user and sending the flight shooting route to the unmanned aerial vehicle remote controller;
the unmanned aerial vehicle remote controller with unmanned aerial vehicle wireless connection, unmanned aerial vehicle remote controller control unmanned aerial vehicle according to flight shooting air route flight.
Optionally, the oblique photography component comprises five cameras.
A geological information identification and extraction method comprises the following steps:
acquiring an oblique image of a research area by adopting an unmanned aerial vehicle aerial photography technology;
generating a three-dimensional model of a research area according to the oblique image;
and identifying and extracting geological information based on the three-dimensional model to obtain geological information of a research area.
Optionally, the generating a three-dimensional model of a research area according to the oblique image specifically includes:
performing space triangulation on the oblique image to obtain external orientation elements of the oblique image; the exterior orientation element comprises parameters of a three-dimensional coordinate position and three attitude angles of an optical center of the camera;
performing multi-view image dense matching on the oblique image according to the exterior orientation element to obtain a digital point cloud of a research area;
generating a three-dimensional TIN model of a research area based on the digital point cloud;
and carrying out texture attachment on the three-dimensional TIN model by adopting the texture image of the research area to obtain the three-dimensional model of the research area.
Optionally, the identifying and extracting geological information based on the three-dimensional model to obtain geological information of a research area specifically includes:
measuring the three-dimensional model to obtain longitude and latitude, elevation, horizontal distance between adjacent monitoring points and vertical distance between adjacent monitoring points of a research area;
determining a fault of a research area according to the geological condition of the three-dimensional model;
calculating the trend, the inclination and the dip angle of each fault in a triangulation mode;
determining properties of each rock formation of the investigation region based on the distribution and color of the rock formations in the three-dimensional model;
determining the dip angle of the rock stratum based on the texture trend of the rock stratum in the three-dimensional model;
and determining the exposed perimeter, surface area and volume of the rock based on the three-dimensional model.
Optionally, the determining the fault of the research region according to the geological condition of the three-dimensional model specifically includes:
determining discontinuous positions of rock layers, stratums and rocks in the three-dimensional model in the horizontal or vertical direction as first fault positions;
determining the position of the three-dimensional model where the fold and the broken outcrop line are mistakenly broken as a second fault position;
and determining the position of the stratum missing or the stratum repeating with the same sequence in the stratum inclination direction of the three-dimensional model as a third fault position.
Optionally, adopt unmanned aerial vehicle aerial photography technique to acquire the oblique image in research area, still include before:
determining the regional appearance of a research region based on Google map software, and selecting monitoring points of the research region;
and generating a flight shooting route based on the monitoring points.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a geological information recognition and extraction system and a geological information recognition and extraction method.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings needed to be used in the embodiments will be briefly described below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings without inventive exercise.
FIG. 1 is a structural diagram of a geological information recognition and extraction system according to the present invention;
FIG. 2 is a flow chart of a method for identifying and extracting geological information according to the present invention;
FIG. 3 is a schematic diagram of the present invention providing a three-dimensional model for generating a region of interest from oblique images;
FIG. 4 is a schematic diagram of a three-dimensional model provided in accordance with an embodiment of the present invention;
FIG. 5 is a schematic diagram of an accurate three-dimensional model provided by an embodiment of the present invention;
FIG. 6 is a schematic diagram illustrating a principle of calculating the trend and trend of each fault by triangulation according to an embodiment of the present invention;
FIG. 7 is a schematic diagram illustrating a principle of calculating fault dip according to fault strike and dip according to an embodiment of the present invention;
FIG. 8 is a schematic diagram of a determination of a property of a formation provided by an embodiment of the present invention;
FIG. 9 is a graph comparing properties of different rock formations provided by embodiments of the present invention;
FIG. 10 is a schematic diagram of formation dip determination provided by an embodiment of the present invention;
fig. 11 is a schematic diagram of the calculation of the exposed volume of rock according to the embodiment of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
The invention aims to provide a system and a method for identifying and extracting geological information so as to improve the efficiency and comprehensiveness of geological exploration.
In order to make the aforementioned objects, features and advantages of the present invention comprehensible, embodiments accompanied with figures are described in further detail below.
In order to achieve the purpose, the invention provides the following scheme:
as shown in fig. 1, the present invention provides a system for identifying and extracting geological information, comprising: the system comprises an unmanned aerial vehicle 1, an unmanned aerial vehicle control terminal 2, an oblique photography component 3, a three-dimensional model generation module 4 and a geological information identification and extraction module 5; the unmanned aerial vehicle control terminal 2 is in wireless connection with the unmanned aerial vehicle 1; the unmanned aerial vehicle control terminal 2 is used for controlling the flight of the unmanned aerial vehicle 1; the oblique photographing component 3 is arranged on the unmanned aerial vehicle 1, the oblique photographing component 3 is in wireless connection with the three-dimensional model generation module 4, and the oblique photographing component 3 is used for acquiring an oblique image of a research area and sending the oblique image to the three-dimensional model generation module 4; the three-dimensional model generation module 4 is connected with the geological information identification and extraction module 5, and the three-dimensional model generation module 4 is used for generating a three-dimensional model of a research area according to the oblique image and sending the three-dimensional model to the geological information identification and extraction module 5; and the geological information identification and extraction module 5 is used for identifying and extracting geological information based on the three-dimensional model to obtain geological information of a research area.
The unmanned aerial vehicle control terminal 2 comprises a mobile phone terminal and an unmanned aerial vehicle remote controller; the mobile phone mobile terminal is provided with DJI GO software and is in wireless connection with the unmanned aerial vehicle remote controller, and the mobile phone mobile terminal is used for generating a flight shooting route according to flight parameters input by a user and sending the flight shooting route to the unmanned aerial vehicle remote controller; the unmanned aerial vehicle remote controller with unmanned aerial vehicle wireless connection, unmanned aerial vehicle remote controller control unmanned aerial vehicle according to flight shooting air route flight. The oblique photography component comprises five cameras.
As shown in fig. 2, the present invention further provides a method for identifying and extracting geological information, wherein the method for identifying and extracting geological information comprises the following steps:
As a specific embodiment, through Google map software, longitude and latitude are input to position a region to be researched, the appearance of the region is displayed, a representative place with complex geological conditions is selected in the research region to serve as a monitoring point for extracting geological information, geographical information such as elevation, terrain, area and the like is consulted, and geological overview of the region including stratum, structural characteristics and the like is known. Determining the type and model of the unmanned aerial vehicle to be used, selecting the optimal time for carrying out the aerial photography measurement work of the unmanned aerial vehicle according to the geological condition and meteorological conditions of an investigation region, and selecting the unmanned aerial vehicle to go to an observation point to carry out the aerial photography work of the unmanned aerial vehicle when the meteorological conditions are suitable.
After arriving the field region of taking photo by plane, link to each other mobile device cell-phone and unmanned aerial vehicle remote controller, each part and the instrument equipment situation of inspection unmanned aerial vehicle ensure that every part of unmanned aerial vehicle can both effectively operate. And opening a mobile phone software DJIGO flight control tool to perform trial flight operation. An unmanned flat and open place is selected as a take-off and landing place, so that the safety of aerial photography personnel is ensured to the maximum extent, and the smooth take-off and landing of the airplane is ensured.
After the pilot flight is finished, the aerial photography of the research area is prepared. Opening DJIGO flight control tool software, determining an aerial photographing range according to requirements, and inputting the following parameter data:
aerial photography area: generally aerial range length and width data.
II, course overlapping rate: the course overlapping degree can influence the number of photos, and the higher the overlapping degree is, the more photos are.
Third, the side overlapping rate: the side overlapping degree can affect the number of photos taken, the length of flight path and the flight time.
Camera tilt angle (0: vertical down): and selecting whether the direction of the unmanned aerial vehicle head is parallel to the flight line or perpendicular to the flight line during aerial photography according to requirements. The difference between the two influences the length of the flight path, the flight time and the number of photos taken.
Flight height: and adjusting the flight height of aerial photography according to the requirement. In the same area, the flight height affects the ground resolution, flight duration, flight path length, and the number of photos taken.
Flight speed: the flight speed affects the duration of the flight.
The above parameters are respectively the flight area and the control determined by the route planning. DJI GO flight control tool software automatically generates a flight shooting course according to the parameters, and a camera shoots a defined area according to the course.
The shooting process adopts the oblique photogrammetry technique, and the flight platform is carried with a plurality of sensors, and can acquire images from five different angles simultaneously. The unmanned aerial vehicle automatically hovers in the air at each route shooting point, and automatically navigates back when all the aerial lines are shot. If unmanned aerial vehicle shoots in-process electric quantity not enough, will return to the journey automatically, operating personnel changes the backup battery after, restart, unmanned aerial vehicle will fly back automatically and shoot the breakpoint, continue the task of taking photo by plane. In the unmanned aerial vehicle working process of taking photo by plane, the mobile device that links to each other with the remote controller can present the image of taking photo by plane in real time, and ground operating personnel adjusts the parameter of camera with the mobile device according to actual need, like the light ring etc. to obtain more clear, the saturated image of color.
The unmanned aerial vehicle flight instruction after the task is completed comprises the following steps: the software can select four actions of returning to the takeoff position and landing, hovering (keeping a hovering state at a task ending position), ending point landing (landing at the task ending position), and returning to the starting point (returning to the task starting position and hovering). After the flight is finished, the quality of aerial photos obtained by field flight needs to be checked, the inclination and the course deviation of an aircraft camera can be caused by the instability of airflow, the aerial photos are shot again when the quality of the aerial photos does not meet the requirements, and the precision and the speed of post data processing can be obviously improved by high-quality original data.
As a specific embodiment, the processing of the image data of the aerial photography by the unmanned aerial vehicle mainly considers the need of generating a three-dimensional model, so a three-dimensional live-action modeling software Context Capture is used. The method can automatically generate three-dimensional point cloud data, an orthoimage and a DSM (Digital satellite System) image from simple continuous images, and generate an accurate geographic reference Model by using ground control points or GPS (global positioning system) labels. The project to be made can be located, and the coordinate, length, area and volume can be measured more accurately. All pictures can be fully checked by automatically recognizing the relative position and direction of each picture. And finally, manufacturing the most real three-dimensional live-action model by utilizing automatic three-dimensional reconstruction, texture mapping and reprocessing of the binding relationship and reconstruction constraint.
As shown in fig. 3, the model generation process is divided into the following steps: preparing original data, starting software and importing photos, performing aerial triangulation, generating dense point cloud, constructing a TIN (Triangulated Irregular Network) model, automatically mapping texture slices and generating a three-dimensional model. The main process is as follows:
firstly, raw data and pretreatment: before processing, screening out qualified data including flight path data, flight parameters and original photos with normal saturation and color of the unmanned aerial vehicle, and removing unqualified aerial photos, such as serious exposure, failure in focusing and the like. Before the original photos are imported into the software, the software automatically identifies the relative position and direction of each photo without any editing operation of changing the size, cutting and rotating, and fully corrects all the photos.
Starting software and importing photos: and sequentially starting the Context Capture master and the Context Capturengine software, newly building a project and importing the photos. The Context Capture master is mainly software for manual operation, the software Context Capture engine runs in the background of the computer, the software does not need to communicate and interact with users, and when the engine end of the engine is idle, the execution of tasks in a waiting queue is mainly determined by the priority and uploaded data. And (3) setting parameters in the importing process: set down sampling (Set sampling rate): the parameters only resample the picture in the process of space three, and the original resolution image is still used in modeling. Check images Integrity: this function can be used for data integrity checks when modeling fails. Import positions (Import POS): import POS format is as follows: if there are multiple groups of photos (photomoup), it is necessary to ensure that the name of the photos in each group of photos is unique, and the POS path must be in english.
Measuring the aerial triangulation: and right clicking the newly built engineering block, clicking and submitting aerial triangulation, automatically generating a new block, starting aerial triangulation calculation, and opening engine software to check the process of aerial triangulation. And after the ContextCapture is completed in the air triangulation, generating an air three-dimensional report which can be directly used for next matching and three-dimensional modeling.
The aim of calculating the aerial triangulation of the oblique image is to acquire the external orientation elements of the image, namely the three-dimensional coordinate position of the optical center of the camera and the parameters of three attitude angles. The empty three-dimensional processing is a core link of modeling, POS data carried by oblique photography photos is used as an initial orientation element, and the exterior orientation element of each photo can be solved according to a collinear equation. The connection points between the images are generated by utilizing multi-baseline multi-feature matching, and oblique photography aerial triangulation can be completed through a small number of ground control points and block adjustment. The parameters in the three empty steps were determined as follows:
keypoint dense (feature point density): normal default
QR code extraction (whether to extract a two-dimensional code target): disabled
Pair selection mode (matching mode): default
Component construction mode (Component construction mode): one-pass
Blockwise color equalisation (homogenisation of the blocks): enabled
Tie point (feature point extraction method): computer
Position: adjust
Rotation: adjust
Optical properties evaluation mode (Optical property estimation mode): one-pass
Focal length: adjust
Principal point (emphasis): adjust
Radial deformation: adjust
Tangential deformation: keep
For aerial photographs, default parameters are typically used. If there are multiple stands and there is a case of inconsistent altitude, two parameter options "Pair selection mode" and "Component construction mode" may be modified.
Generating model
And after the calculation of the three-in-one, newly building a reconstruction project, starting reconstruction in the three-in-one result, and constructing the model by using a 'New retrieval' button at the lower right corner in a 'General' tab. In this case, two parameters, namely, spatial frame (spatial frame) and Processing settings (Processing settings) need to be set, and the maximum and minimum values of the three-dimensional spatial frame are generally set X, Y, Z in accordance with actual conditions. According to the air-three encrypted information result, high-density digital point cloud can be obtained through multi-view image dense matching. When the cloud amount of the dense point is large, the reconstruction item can be divided into a plurality of tiles according to the performance of the computer to realize the TIN model construction under different levels of detail. The size of the tile can be set by itself, and the evaluation value for calculating the size of the required memory varies with the size of the tile. By adjusting the triangle size to match the original image resolution, the size of the required memory is controlled to be about 50% of the physical memory, and meanwhile, the triangulation network in a flat area is simplified, so that a three-dimensional TIN model is obtained. The three-dimensional TIN model is shown in FIG. 4:
after the TIN model is constructed, the three-dimensional TIN model and the texture image need to be registered and attached with the texture. And selecting a texture image suitable for the triangulation network model by calculating the included angle between the normal direction of each triangular surface of the TIN and the photograph containing the geologic body. The smaller the included angle is, the closer the triangular surface is to the image plane, the more matched the triangular surface is, and the higher the texture quality is. And observing the generated three-dimensional model in the Acute3D Viewer, wherein the maximum texture size in the three-dimensional scene model is 8192 pixels, and the detail level type is a single level. Meanwhile, the three-dimensional model can be observed in different azimuth angles and in a magnifying and shrinking manner, the spatial position, the shape, the color, the appearance and the like of the ground objects in the scene are basically consistent with the actual environment, the connection among all the ground object monomers is smooth and complete, the contour of the ground objects is clear, and the actual situation is basically met. However, in the three-dimensional model, it can be found that the edges of some trees and buildings are deformed, the side texture of the ground features is blurred, and the like, because of occlusion in the shooting process, and because homogeneous ground features such as vegetation and water surfaces lack obvious feature points, the matching of images of the same name is less, and thus the precision of the three-dimensional model is low, and the texture of slices is lost and misplaced. After the three-dimensional model is subjected to the graphic processing, an accurate three-dimensional model diagram can be finally obtained, as shown in fig. 5.
And 203, identifying and extracting geological information based on the three-dimensional model to obtain geological information of a research area.
The generated three-dimensional model is observed in the ace 3D Viewer, and spatial features such as shape, size, texture, and position are listed. And measuring the land feature data according to the color tone, the shape and the structure of the geologic body in the three-dimensional real-scene model and by combining with the comprehensive analysis of landform, hydrology, vegetation and the like connected with the geologic body.
The method mainly adopts a method of manual visual interpretation and man-machine interactive interpretation, before geological information such as fracture, lithology structure and the like is extracted, a correct identification interpretation mark is established according to geological data so as to support the accuracy of geological information acquisition, and result verification can be carried out on geological samples of rocks and other real objects which can be subjected to field investigation.
Step 203 specifically includes: and measuring the three-dimensional model to obtain the longitude and latitude, the elevation, the horizontal distance between the adjacent monitoring points and the vertical distance between the adjacent monitoring points of the research area.
As a specific embodiment, the Acute3D viewer is opened, and position data such as the longitude and latitude, the elevation, the distance between two points, the vertical distance and the like of an area can be directly extracted from the three-dimensional live-action model by moving a cursor and applying a ruler measuring tool.
Determining a fault of a research area according to the geological condition of the three-dimensional model; calculating the trend, the inclination and the dip angle of each fault in a triangulation mode; the determining of the fault of the research area according to the geological condition of the three-dimensional model specifically comprises the following steps: determining discontinuous positions of rock layers, stratums and rocks in the three-dimensional model in the horizontal or vertical direction as first fault positions; determining the position of the three-dimensional model where the fold and the broken outcrop line are mistakenly broken as a second fault position; and determining the position of the stratum missing or the stratum repeating with the same sequence in the stratum inclination direction of the three-dimensional model as a third fault position.
As a specific example, it is first determined whether there is a fault: if the phenomenon that the rock stratum, the stratum and the rock are discontinuous in the horizontal or vertical direction is observed in the model, a fault is determined to exist; the model is determined to be that the outcrop line of geological structures such as folds, fractures and the like is broken, and the situation that the trend of the texture is discontinuous indicates that the fault passes through the position; in the direction of the stratum inclination, a stratum missing phenomenon occurs, or stratums with the same sequence repeatedly occur, and the like, so that the fault is clearly shown to exist. The large-scale geological structure can be observed by a definition and judgment method, and the method is mainly manual visual inspection.
As shown in FIG. 6, the fault of the three-dimensional model, such as the area enclosed by triangle frames of Δ ABC and Δ ABC is a fault cliff and represents a fault triangular surface, the position of the fault cliff is the upper plate of the fault, the slope bottom line of the fault triangular surface is a fault line, the intersection line of the fault plane and the horizontal plane is a trend line, and the trend data of the fault is measured along the extension direction of the trend line, such as the fault trend of the exemplary model is 335 degrees. The line perpendicular to the strike line on the fault plane is an inclined line, the orientation of the inclined line on the horizontal plane, which is indicated by the projection of the inclined line on the horizontal plane, is inclined to the fault, and the orientation parameter of the inclined line on the horizontal plane, which is indicated by the projection of the inclined line on the horizontal plane, is inclined to the fault, for example, the fault inclination of the model is 65 degrees.
The dip angle value needs to be calculated, longitude, latitude and elevation parameters of 6 triangular points including the points A, B, C, a, B and C are obtained through the Acute3D Viewer respectively, and are arranged into a table for the next dip angle calculation.
As shown in fig. 7, run from a known fault, dip. The fault triangular plane dip angle can be obtained by calculation according to the extracted information, and the calculation method comprises the following steps: taking triangular face ABC as an example, θ is the inclination angle of triangular face ABC, AD is perpendicular to BC, and AE is the perpendicular distance from point A to face BCE. Measuring AD and AE length values by using an Acute3Dviewer ruler measuring tool, and using a formula:
θ＝arcsin(AE/AD)
the inclination angle theta of the fault triangular surface can be obtained.
Based on the distribution and color of the rock formations in the three-dimensional model, properties of each rock formation of the investigation region are determined.
As a specific embodiment, for extracting rock property information, a three-dimensional live-action model is observed in an act 3D Viewer, and basic information such as texture endpoint coordinates, longitude and latitude, and elevation can be directly read. The distribution and color condition, the exposure condition and the trend of the rock are observed, the type and the property of the rock are preliminarily judged, whether the rock has the weathering erosion condition and the weathering erosion degree or not is judged, and finally, the rock can be further judged according to the rock sample collected from the site. As shown in fig. 8, the whole rock 1 is observed to be distributed in layers, fully exposed and extended in a strip shape, the trend and the trend of the rock can be clearly seen, the rock is judged to be sedimentary rock, and then the rock can be judged to be limestone according to the grey and texture characteristics of the rock, but the overall tone of the limestone is obviously different and has the colors of white, grey and the like on the image, which indicates that the limestone is obviously impure, and the rock is primarily judged to be dolomitic limestone. However, this limestone is cut by two sections of invaded rock, the invaded rock 3 is light in color tone, and the invaded rock 3 is more severely weathered and degraded than the invaded rock 2, and it can be preliminarily judged that the invaded rock 2 is a neutral magma rock, and these two sections of invaded rock are called invaded rock 2 and invaded rock 3, both of which extend in a vein-like manner, and the invaded rock 2 is deeper in color tone than the invaded rock 3 and is a bedrock. However, the definition of the sample cannot be determined to what kind of rock, but further determination can be made according to the collected rock sample. According to the rock sample collected in the field, as shown in fig. 9, the rock 1 is observed to be off-white and have white crystals, and the rock 1 can be judged to be dolomitic limestone. The invaded rock 2 has a distinct spot-like structure, the speckles are plagioclase and hornblende, and the entire rock is gray and grayish green, so that the invaded rock 2 can be further judged as the amphiphanite. The invaded rock 3 was judged to be diabase, since it was observed that the whole color of the invaded rock 3 was grayish black, the main components were pyroxene and feldspar, and it was more easily broken than the invaded rock 2.
And determining the dip angle of the rock stratum based on the texture trend of the rock stratum in the three-dimensional model.
As a specific embodiment, as shown in fig. 10, the textures a-b, c-d, and e-f are marked in fig. 10, the coordinates of the end points of the 6 textures, including longitude, latitude, and elevation parameters, are obtained statistically, and then the distance and vertical distance parameters of three sections are obtained respectively, and a list is sorted, so as to facilitate the calculation of the rock formation dip angle. Then the trend is obtained to be 36 degrees along the extending direction of the rock stratum, the inclination is obtained to be 126 degrees along the inclining direction, and the rock stratum inclination angle can be calculated as follows: if the face abcd represents the rock face, bc is the exposed texture of the rock and be is the vertical distance from point b to the face dc. The formation dip angle θ can be obtained according to the formula: θ is arcsin (be/bc).
And determining the exposed perimeter, surface area and volume of the rock based on the three-dimensional model.
As a specific embodiment, the perimeter, the surface area and the volume value of the exposed rock can be estimated under a custom plane method. The sampling distance, perimeter, and area can be simply measured in the measurement tool of the ace 3D viewer, and the volume is calculated according to the exposed rock surface area, and the volume extraction method is shown in fig. 11: the volume value is determined according to the exposed rock surface area, assuming that the surface ABCD is the exposed surface of the rock, the points A and B are projected to the points E and F on the horizontal plane CDEF, and the volume of the object ABCDEF is the volume obtained by the measuring tool according to the exposed surface.
Through the Acute3D viewer, the obtained geological information comprises the longitude and latitude, the elevation, the distance between two points, the vertical distance, faults (trend, tendency and inclination angle), the type and the property of rock, rock texture information, rock inclination angle, exposed perimeter, surface area, volume and the like of different types of rocks. The digital information can also be used for calculating more geological conditions to obtain more accurate geological data.
According to the specific embodiment provided by the invention, the invention discloses the following technical effects:
the invention provides a geological information recognition and extraction system and a geological information recognition and extraction method.
The embodiments in the present description are described in a progressive manner, each embodiment focuses on differences from other embodiments, and the same and similar parts among the embodiments are referred to each other.
The principles and embodiments of the present invention have been described herein using specific examples, which are provided only to help understand the method and the core concept of the present invention; meanwhile, for a person skilled in the art, according to the idea of the present invention, the specific embodiments and the application range may be changed. In view of the above, the present disclosure should not be construed as limiting the invention.
Claims (8)
1. A system for identifying and extracting geological information, comprising: the system comprises an unmanned aerial vehicle, an unmanned aerial vehicle control terminal, an oblique photography component, a three-dimensional model generation module and a geological information identification and extraction module;
the unmanned aerial vehicle control terminal is wirelessly connected with the unmanned aerial vehicle; the unmanned aerial vehicle control terminal is used for controlling the flight of the unmanned aerial vehicle;
the oblique photographing component is arranged on the unmanned aerial vehicle, is in wireless connection with the three-dimensional model generation module, and is used for acquiring oblique images of a research area and sending the oblique images to the three-dimensional model generation module;
the three-dimensional model generation module is connected with the geological information recognition and extraction module, and is used for generating a three-dimensional model of a research area according to the oblique image and sending the three-dimensional model to the geological information recognition and extraction module;
and the geological information identification and extraction module is used for identifying and extracting geological information based on the three-dimensional model to obtain geological information of a research area.
2. The geological information recognition and extraction system according to claim 1, wherein the unmanned aerial vehicle control terminal comprises a mobile phone terminal and an unmanned aerial vehicle remote controller;
the mobile phone mobile terminal is provided with DJI GO software and is in wireless connection with the unmanned aerial vehicle remote controller, and the mobile phone mobile terminal is used for generating a flight shooting route according to flight parameters input by a user and sending the flight shooting route to the unmanned aerial vehicle remote controller;
the unmanned aerial vehicle remote controller with unmanned aerial vehicle wireless connection, unmanned aerial vehicle remote controller control unmanned aerial vehicle according to flight shooting air route flight.
3. The geological information identification and extraction system according to claim 1, wherein said oblique camera assembly comprises five cameras.
4. A geological information identification and extraction method is characterized by comprising the following steps:
acquiring an oblique image of a research area by adopting an unmanned aerial vehicle aerial photography technology;
generating a three-dimensional model of a research area according to the oblique image;
and identifying and extracting geological information based on the three-dimensional model to obtain geological information of a research area.
5. The method for identifying and extracting geological information according to claim 4, wherein the generating a three-dimensional model of a research area from the oblique image comprises:
performing space triangulation on the oblique image to obtain external orientation elements of the oblique image; the exterior orientation element comprises parameters of a three-dimensional coordinate position and three attitude angles of an optical center of the camera;
performing multi-view image dense matching on the oblique image according to the exterior orientation element to obtain a digital point cloud of a research area;
generating a three-dimensional TIN model of a research area based on the digital point cloud;
and carrying out texture attachment on the three-dimensional TIN model by adopting the texture image of the research area to obtain the three-dimensional model of the research area.
6. The geological information identification and extraction method according to claim 4, wherein the identifying and extracting geological information based on the three-dimensional model to obtain geological information of a research area specifically comprises:
measuring the three-dimensional model to obtain longitude and latitude, elevation, horizontal distance between adjacent monitoring points and vertical distance between adjacent monitoring points of a research area;
determining a fault of a research area according to the geological condition of the three-dimensional model;
calculating the trend, the inclination and the dip angle of each fault in a triangulation mode;
determining properties of each rock formation of the investigation region based on the distribution and color of the rock formations in the three-dimensional model;
determining the dip angle of the rock stratum based on the texture trend of the rock stratum in the three-dimensional model;
and determining the exposed perimeter, surface area and volume of the rock based on the three-dimensional model.
7. The method for identifying and extracting geological information according to claim 6, wherein the determining the fault of the research region according to the geological condition of the three-dimensional model specifically comprises:
determining discontinuous positions of rock layers, stratums and rocks in the three-dimensional model in the horizontal or vertical direction as first fault positions;
determining the position of the three-dimensional model where the fold and the broken outcrop line are mistakenly broken as a second fault position;
and determining the position of the stratum missing or the stratum repeating with the same sequence in the stratum inclination direction of the three-dimensional model as a third fault position.
8. The geological information identification and extraction method according to claim 4, wherein the obtaining of the oblique image of the research area by the unmanned aerial vehicle aerial photography technique further comprises:
determining the regional appearance of a research region based on Google map software, and selecting monitoring points of the research region;
and generating a flight shooting route based on the monitoring points.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112729260A (en) * | 2020-12-15 | 2021-04-30 | 广州极飞科技股份有限公司 | Surveying and mapping system and surveying and mapping method |
| CN115471615A (en) * | 2022-10-10 | 2022-12-13 | 中国矿业大学 | Ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging |
| CN115688491A (en) * | 2022-12-30 | 2023-02-03 | 长江水利委员会长江科学院 | Water conservancy digital twin simulation method based on block chain |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1858803A (en) * | 2006-04-04 | 2006-11-08 | 天津大学 | Three dimension uniform model construction method of water conservancy hydropower engineering geological information |
| CN106767720A (en) * | 2016-12-30 | 2017-05-31 | 广州地理研究所 | Single-lens oblique photograph measuring method, device and system based on unmanned plane |
| CN108665536A (en) * | 2018-05-14 | 2018-10-16 | 广州市城市规划勘测设计研究院 | Three-dimensional and live-action data method for visualizing, device and computer readable storage medium |
| CN109472866A (en) * | 2018-10-17 | 2019-03-15 | 四川省地质工程勘察院 | A kind of modeling method of big geology Building Information Model |
| CN109916379A (en) * | 2019-04-12 | 2019-06-21 | 中国石油大学(华东) | A kind of field geology investigation new method appeared based on number |
| CN110319816A (en) * | 2018-03-29 | 2019-10-11 | 上海勘测设计研究院有限公司 | Geological record system based on photogrammetric technology and edit and record method |
-
2020
- 2020-04-28 CN CN202010352279.5A patent/CN111540049A/en active Pending
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN1858803A (en) * | 2006-04-04 | 2006-11-08 | 天津大学 | Three dimension uniform model construction method of water conservancy hydropower engineering geological information |
| CN106767720A (en) * | 2016-12-30 | 2017-05-31 | 广州地理研究所 | Single-lens oblique photograph measuring method, device and system based on unmanned plane |
| CN110319816A (en) * | 2018-03-29 | 2019-10-11 | 上海勘测设计研究院有限公司 | Geological record system based on photogrammetric technology and edit and record method |
| CN108665536A (en) * | 2018-05-14 | 2018-10-16 | 广州市城市规划勘测设计研究院 | Three-dimensional and live-action data method for visualizing, device and computer readable storage medium |
| CN109472866A (en) * | 2018-10-17 | 2019-03-15 | 四川省地质工程勘察院 | A kind of modeling method of big geology Building Information Model |
| CN109916379A (en) * | 2019-04-12 | 2019-06-21 | 中国石油大学(华东) | A kind of field geology investigation new method appeared based on number |
Non-Patent Citations (2)
| Title |
|---|
| 周良辰 等: "平面地质图的三维地质体建模方法研究", 地球信息科学学报, vol. 15, no. 1 * |
| 胡树华: "断煤交线与断层走向夹角计算公式的推导", 江西煤炭科技, no. 4 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN112729260A (en) * | 2020-12-15 | 2021-04-30 | 广州极飞科技股份有限公司 | Surveying and mapping system and surveying and mapping method |
| CN115471615A (en) * | 2022-10-10 | 2022-12-13 | 中国矿业大学 | Ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging |
| CN115471615B (en) * | 2022-10-10 | 2023-09-26 | 中国矿业大学 | Ultra-deep hole geological imaging method based on unmanned aerial vehicle aerial photography imaging |
| CN115688491A (en) * | 2022-12-30 | 2023-02-03 | 长江水利委员会长江科学院 | Water conservancy digital twin simulation method based on block chain |
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